%run ./setup_notebook.ipynb
!pip install wordsegment
Requirement already satisfied: wordsegment in c:\users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages (1.3.1)
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from collections import defaultdict
import json
import ast
from tqdm import tqdm
import time
import requests
from bs4 import BeautifulSoup, SoupStrainer
import pprint
import logging
import re
# from kaggle_secrets import UserSecretsClient
import plotly.express as px
import plotly.io as pio
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import swifter
import itertools
from geopy.geocoders import Nominatim
from wordsegment import load, segment
# from nltk.stem.snowball import SnowballStemmer
# from nltk.corpus import stopwords
# from nltk.tokenize import word_tokenize
# from nltk.stem import WordNetLemmatizer
# STOPWORDS = set(stopwords.words('english'))
px.defaults.template = 'bnw'
df_linkedin_listing_usa = pd.read_csv("../linkedin-data-analyst-jobs-listings/linkedin-jobs-usa.csv")
df_linkedin_listing_usa["country"] = "USA"
df_linkedin_listing_canada = pd.read_csv("../linkedin-data-analyst-jobs-listings/linkedin-jobs-canada.csv")
df_linkedin_listing_canada["country"] = "Canada"
assert list(df_linkedin_listing_usa.columns) == list(df_linkedin_listing_canada.columns), \
"Columns not equal, cannot concat vertically"
df_linkedin_listing = pd.concat([df_linkedin_listing_usa, df_linkedin_listing_canada])
df_linkedin_listing.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 5618 entries, 0 to 2772 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 title 5618 non-null object 1 company 5618 non-null object 2 description 5618 non-null object 3 onsite_remote 5618 non-null object 4 salary 965 non-null object 5 location 5618 non-null object 6 criteria 5618 non-null object 7 posted_date 5618 non-null object 8 link 5618 non-null object 9 country 5618 non-null object dtypes: object(10) memory usage: 482.8+ KB
df_linkedin_listing.head(5)
| title | company | description | onsite_remote | salary | location | criteria | posted_date | link | country | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Data Analyst - Recent Graduate | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | Buffalo-Niagara Falls Area | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-22 | https://www.linkedin.com/jobs/view/data-analys... | USA |
| 1 | Data Analyst - Recent Graduate | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | San Jose, CA | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-22 | https://www.linkedin.com/jobs/view/data-analys... | USA |
| 2 | Data Analyst | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | Texas, United States | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-17 | https://www.linkedin.com/jobs/view/data-analys... | USA |
| 3 | Data Analyst | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | Illinois, United States | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-17 | https://www.linkedin.com/jobs/view/data-analys... | USA |
| 4 | Entry-Level Data Analyst | The Federal Savings Bank | The Federal Savings Bank, a national bank and ... | onsite | NaN | Chicago, IL | [{'Seniority level': 'Entry level'}, {'Employm... | 2022-11-17 | https://www.linkedin.com/jobs/view/entry-level... | USA |
I noticed that there are some unnormalized JSONs in the criteria column. I will normalize the criteria column and find out the nullity of the normalized criteria column.
Let's normalize the criteria column
df_linkedin_listing.head(1)
| title | company | description | onsite_remote | salary | location | criteria | posted_date | link | country | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Data Analyst - Recent Graduate | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | Buffalo-Niagara Falls Area | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-22 | https://www.linkedin.com/jobs/view/data-analys... | USA |
criteria_records = []
for criteria_string_form in df_linkedin_listing["criteria"]:
criteria_array = None
try:
criteria_array = ast.literal_eval(criteria_string_form)
except Exception as e:
print(e)
print(criteria_string_form)
continue
criteria_records.append({k:v for criteria_dict in criteria_array for k,v in criteria_dict.items()})
# criteria_df = df_linkedin_listing.iloc[0:3].apply(convert_jsons_to_table, axis=1)
criteria_records[0]
{'Seniority level': 'Not Applicable',
'Employment type': 'Full-time',
'Job function': 'Information Technology',
'Industries': 'Software Development, Technology, Information and Internet, and Financial Services'}
criteria_df = pd.DataFrame.from_records(criteria_records)
# Validation Cell
criteria_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5618 entries, 0 to 5617 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Seniority level 5194 non-null object 1 Employment type 5541 non-null object 2 Job function 5194 non-null object 3 Industries 5193 non-null object dtypes: object(4) memory usage: 175.7+ KB
df = pd.merge(df_linkedin_listing, criteria_df, left_index=True, right_index=True)
df.head(1)
| title | company | description | onsite_remote | salary | location | criteria | posted_date | link | country | Seniority level | Employment type | Job function | Industries | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Data Analyst - Recent Graduate | PayPal | At PayPal (NASDAQ: PYPL), we believe that ever... | onsite | NaN | Buffalo-Niagara Falls Area | [{'Seniority level': 'Not Applicable'}, {'Empl... | 2022-11-22 | https://www.linkedin.com/jobs/view/data-analys... | USA | Not Applicable | Full-time | Information Technology | Software Development, Technology, Information ... |
Looks good! Let's drop the criteria column which holds the json that we have already normalized into other columns
df.drop("criteria", axis=1, inplace=True)
df.reset_index(drop=True, inplace=True)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5618 entries, 0 to 5617 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 title 5618 non-null object 1 company 5618 non-null object 2 description 5618 non-null object 3 onsite_remote 5618 non-null object 4 salary 965 non-null object 5 location 5618 non-null object 6 posted_date 5618 non-null object 7 link 5618 non-null object 8 country 5618 non-null object 9 Seniority level 5241 non-null object 10 Employment type 5527 non-null object 11 Job function 5241 non-null object 12 Industries 5239 non-null object dtypes: object(13) memory usage: 570.7+ KB
Let's change those to Nan
df["Seniority level"] = df["Seniority level"].replace("Not Applicable", np.nan)
df["Seniority level"].info()
<class 'pandas.core.series.Series'> RangeIndex: 5618 entries, 0 to 5617 Series name: Seniority level Non-Null Count Dtype -------------- ----- 3841 non-null object dtypes: object(1) memory usage: 44.0+ KB
df.to_csv("Linkedin_Jobs_American_and_Canadian.csv")
Let's use Wandb to make understanding fundamental data easier
Credit to https://www.kaggle.com/code/ayuraj/interactive-eda-using-w-b-tables/notebook for code and inspiration
# !pip install -q --upgrade wandb
# # Import wandb
# import wandb
# try:
# from kaggle_secrets import UserSecretsClient
# user_secrets = UserSecretsClient()
# secret_value_0 = user_secrets.get_secret("wandb_api")
# wandb.login(key=secret_value_0)
# anony=None
# except Exception as e:
# anony = "must"
# print('If you want to use your W&B account, go to Add-ons -> Secrets and add your W&B access token. Use the Label name as "wandb_api". \nGet your W&B access token from here: https://wandb.ai/authorize')
# run = wandb.init(project='eda', anonymous=None) # W&B Code 1
# # Initialize a W&B run to log images
# data_at = wandb.Table(columns=df.columns.tolist()) # W&B Code 2
# for i in tqdm(range(len(df))):
# row = df.loc[i]
# data_at.add_data(*tuple(row.values[0:])) # W&B Code 3
# wandb.log({'LinkedIn Job Data': data_at}) # W&B Code 4
# wandb.finish() # W&B Code 5
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5618 entries, 0 to 5617 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 title 5618 non-null object 1 company 5618 non-null object 2 description 5618 non-null object 3 onsite_remote 5618 non-null object 4 salary 965 non-null object 5 location 5618 non-null object 6 posted_date 5618 non-null object 7 link 5618 non-null object 8 country 5618 non-null object 9 Seniority level 3841 non-null object 10 Employment type 5527 non-null object 11 Job function 5241 non-null object 12 Industries 5239 non-null object dtypes: object(13) memory usage: 570.7+ KB
fig1 = px.pie(df["title"].value_counts().reset_index(), names="index", values="title", color="index",
color_discrete_sequence=px.colors.qualitative.Pastel2)
remove_px_attributes(fig1)
fig1.update_traces(textinfo=None, textposition="inside")
fig1.add_annotation(text=f"Sample Size: {sum(~df['title'].isnull())}", xref="paper", yref="paper", xanchor="right",
yanchor="top", x=1, y=-0.1, ax=0, ay=0)
fig1.update_layout(
uniformtext_minsize=9, uniformtext_mode="hide",
title=dict(
text="Types of Jobs in Dataset",
x=0.05,
xanchor="left"
), margin=dict(t=100))
df["posted_date"] = df["posted_date"].astype(str)
fig2 = px.line(df["posted_date"].value_counts().sort_index())
fig2.update_yaxes(title="Number of Jobs")
fig2.update_xaxes(title="Posted Date")
fig2.update_layout(title="Number of Jobs Over Time", margin=dict(r=50))
fig2.update_traces(hovertemplate="<b>Date:</b> %{x}<br><b>Number of Jobs:</b> %{y}<br>", mode="lines+markers")
fig2.add_annotation(text=f"Sample Size: {sum(~df['posted_date'].isnull())}", xref="paper", yref="paper", xanchor="right",
yanchor="top", x=1, y=-0.1, ax=0, ay=0)
fig2
fig3 = px.histogram(df, x="onsite_remote", color="onsite_remote")
remove_px_attributes(fig3)
add_annotation_for_figure(fig3, f"Sample Size: {sum(~df['onsite_remote'].isnull())}",
x_anchor="right", x=1)
fig3.update_yaxes(title=dict(text="count", standoff=20))
fig3.update_xaxes(title="onsite_remote")
fig3.update_layout(title="Number of Jobs by Work Style", margin=dict(l=100), coloraxis_showscale=False)
fig4 = px.pie(df["Industries"].value_counts().reset_index(), names="index", values="Industries",
color_discrete_sequence=px.colors.qualitative.Set3)
remove_px_attributes(fig4)
add_annotation_for_figure(fig4, f"Sample Size: {sum(df['Industries'].isnull() == False)}",
x_anchor="right", x=1)
fig4.update_layout(
uniformtext_minsize=12, uniformtext_mode="hide", title=dict(
text="Jobs by Sector",
xanchor="left",
font_size=20,
x=0.05,
y=0.95)
)
fig4.update_traces(textposition="inside", texttemplate="%{percent}")
fig4
# for i, trace in enumerate(fig4.data):
# print(trace["name"])
# if not trace["name"] in items_to_show:
# fig4.data[i]["showlegend"] = False
fig5 = px.pie(df["Job function"].value_counts().reset_index(), names="index", values="Job function",
color_discrete_sequence=px.colors.qualitative.Set3)
remove_px_attributes(fig5)
add_annotation_for_figure(fig5, f"Sample Size: {sum(df['Job function'].isnull() == False)}",
x_anchor="right", x=1)
fig5.update_layout(
uniformtext_minsize=12, uniformtext_mode="hide", title=dict(
text="Jobs by Function",
xanchor="left",
font_size=20,
x=0.05,
y=0.95)
)
fig5.update_traces(textposition="inside", texttemplate="%{percent}")
fig5
# for i, trace in enumerate(fig4.data):
# print(trace["name"])
# if not trace["name"] in items_to_show:
# fig4.data[i]["showlegend"] = False
fig6 = px.histogram(df, x="Employment type", color="Employment type")
remove_px_attributes(fig6)
add_annotation_for_figure(fig6, f"Jobs by Employment Type: {sum(df['Employment type'].isnull() == False)}",
x_anchor="right", x=1)
fig6.update_yaxes(title=dict(text="count", standoff=20))
fig6.update_xaxes(title="Employment type")
fig6.update_layout(title="Number of Jobs by Employment Type", margin=dict(l=100), coloraxis_showscale=False)
df.loc[~df["salary"].isnull(), "salary"]
16 $100,000.00\r\n -\r\n $1...
48 $50,000.00\r\n -\r\n $55...
70 $100,000.00\r\n -\r\n $1...
122 $100,000.00\r\n -\r\n $1...
176 $100,000.00\r\n -\r\n $1...
...
5607 $30.00\r\n -\r\n $33.00
5609 $120,000.00\r\n -\r\n $1...
5614 $75,000.00\r\n -\r\n $95...
5616 $85,000.00\r\n -\r\n $95...
5617 $130,000.00\r\n -\r\n $1...
Name: salary, Length: 965, dtype: object
df_salary = pd.DataFrame()
salary_data = df["salary"].replace("[\r\n\s,]+", "", regex=True) \
.replace("CA", "", regex=True) # replace spaces and unexpected text
salaries = salary_data.str.split("-")
## Left bound and upper bound salaries
df_salary["salary_lb"] = salaries.str[0].str.strip("$").astype(float)
df_salary["salary_ub"] = salaries.str[1].str.strip("$").astype(float)
df_salary["salary_text"] = salary_data
df_salary.head()
| salary_lb | salary_ub | salary_text | |
|---|---|---|---|
| 0 | NaN | NaN | NaN |
| 1 | NaN | NaN | NaN |
| 2 | NaN | NaN | NaN |
| 3 | NaN | NaN | NaN |
| 4 | NaN | NaN | NaN |
df_salary.describe()
| salary_lb | salary_ub | |
|---|---|---|
| count | 965.000000 | 965.000000 |
| mean | 50985.354238 | 61313.719047 |
| std | 49165.472000 | 58034.939059 |
| min | 22.000000 | 24.000000 |
| 25% | 40.000000 | 60.000000 |
| 50% | 60000.000000 | 80000.000000 |
| 75% | 90000.000000 | 115000.000000 |
| max | 135000.000000 | 155000.000000 |
fig = px.histogram(df_salary, x="salary_lb")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_lb'].isnull())}")
fig
We can see the salary values are bimodal, perhaps even trimodal. Some salaries are written as hourly salaries, while some appear to be monthly and other salaries appear to be annual.
Let's break down the salary ranges
df_salary_hourly = df_salary.loc[df_salary["salary_lb"] <= 2000]
df_salary_hourly.describe()
| salary_lb | salary_ub | |
|---|---|---|
| count | 411.000000 | 411.000000 |
| mean | 42.109100 | 52.807494 |
| std | 12.739683 | 19.832016 |
| min | 22.000000 | 24.000000 |
| 25% | 30.000000 | 33.000000 |
| 50% | 40.000000 | 60.000000 |
| 75% | 50.000000 | 60.000000 |
| max | 135.000000 | 145.000000 |
df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000)]
| salary_lb | salary_ub | salary_text | |
|---|---|---|---|
| 198 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 284 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 346 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 438 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 486 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 586 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 686 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 738 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 798 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 886 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 936 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1084 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1134 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1332 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1382 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1532 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1582 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1636 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1748 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1782 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
| 1894 | 5800.0 | 6000.0 | $5800.00-$6000.00 |
I think it's safe the say that the salary date being provided is multimodal. Let's convert these salaries to annual salaries
df_salary.loc[df_salary["salary_lb"] <= 2000, ["salary_lb", "salary_ub"]] = df_salary.loc[df_salary["salary_lb"] <= 2000, ["salary_lb", "salary_ub"]] * 40 * 4 * 12
df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000), ["salary_lb", "salary_ub"]] = \
df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000), ["salary_lb", "salary_ub"]] * 12
df_salary["salary_text"] = "$" + df_salary["salary_lb"].astype(str) + "-" + df_salary["salary_ub"].astype(str)
Check distribution of salaries again
fig = px.histogram(df_salary, x="salary_lb")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_lb'].isnull())}")
fig
fig = px.histogram(df_salary, x="salary_ub")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_ub'].isnull())}")
fig
much better!
df_salary_analysis = df_salary.merge(df, left_index=True, right_index=True)
for column in df_salary_analysis:
print(f"{column}:", len(df_salary_analysis[column].unique()))
salary_lb: 29 salary_ub: 34 salary_text: 49 title: 135 company: 305 description: 362 onsite_remote: 3 salary: 49 location: 148 posted_date: 50 link: 5618 country: 2 Seniority level: 5 Employment type: 5 Job function: 54 Industries: 78
df_salary_analysis["Seniority level"].unique()
array([nan, 'Entry level', 'Associate', 'Mid-Senior level', 'Executive'],
dtype=object)
df_salary_analysis["Employment type"].unique()
array(['Full-time', nan, 'Contract', 'Temporary', 'Volunteer'],
dtype=object)
df_salary_analysis.groupby("Seniority level").agg({"salary_ub": "mean"})
| salary_ub | |
|---|---|
| Seniority level | |
| Associate | 102122.623907 |
| Entry level | 61621.880342 |
| Executive | NaN |
| Mid-Senior level | 122456.738164 |
encode_df = df_salary_analysis.copy()
encode_df["Seniority level"].replace({"Entry level" : 1, "Associate" : 2, "Mid-Senior level" : 3, "Executive" : 4}, inplace=True)
encode_df["Employment type"].replace({"Volunteer" : 1, "Temporary" : 2, "Contract" : 3, "Full-time" : 4}, inplace=True)
fig = px.imshow(encode_df.corr(), color_continuous_scale="BuGn")
fig.update_layout(title=dict(text="Correlations Between Job Attributes", x=0.05, y=0.96, xanchor="left", font_size=24),
margin=dict(b=160))
Findings:
fig7 = px.box(df_salary_analysis, x="salary_lb", color="Seniority level")
remove_px_attributes(fig7)
add_annotation_for_figure(fig7, f"Sample Size: {min(sum(~df_salary_analysis['salary_lb'].isnull()), sum(~df_salary_analysis['Seniority level'].isnull()))}",
x_anchor="center", x=1.1)
fig7.update_layout(showlegend=True, title="Salary (Lower Bound) Based on Seniority Level")
fig7.update_xaxes(showgrid=True)
fig7.update_traces(hoverinfo="x")
from plotly.subplots import make_subplots
class BreakLoop(Exception):
pass
def generate_subplots(figures, custom_specs=None, desired_rows=None, desired_columns=None, titles=None,
auto_position=True, custom_positions=None):
'''
For each figure, append their traces to new subplot figure
based on the correct row and col indices
'''
NUM_FIGURES = len(figures)
DESIRED_ROWS = desired_rows
DESIRED_COLUMNS = desired_columns
# num_traces = sum([len(figure.data) for figure in figures])
# if desired_rows is not None:
# desired_columns = num_figures // desired_rows + 1
# if desired_columns is not None:
# desired_rows = num_figures // desired_columns + 1
specs = None
specs_positions = None
######### Default Specs ######################
if custom_specs is None:
specs = np.full((DESIRED_ROWS, DESIRED_COLUMNS), {})
specs_positions = [(i,j) for i in DESIRED_ROWS for j in DESIRED_COLUMNS]
k = 0
# Populate all figures from top-left to bottom-right into specs
for i in range(DESIRED_ROWS):
for j in range(DESIRED_COLUMNS):
new_dict = {}
new_dict["type"] = figures[k]["data"][0]["type"]
specs[i][j] = new_dict
k += 1
if k >= NUM_FIGURES:
break
specs = specs.tolist()
######## Custom Specs ########################
else:
specs = custom_specs
# find specs positions based on col_span and row_span
specs_positions = []
if auto_position:
try:
for i, spec_row in enumerate(specs):
for j, spec_value in enumerate(spec_row):
if spec_value is None:
continue
# find first position
specs_positions.append((i+1, j+1))
raise BreakLoop("Broke out of Pythonic Loop") # Python is very bad at breaking out of a double for loop
except BreakLoop as e:
pass
row, col = specs_positions.pop()
# Work with Pythonic grid instead of Plotly grid, which starts at (1,1) top-left instead of (0,0) top-left
row -= 1
col -= 1
for _ in range(NUM_FIGURES):
this_figure_row = row
this_figure_column = col
specs_dict = specs[row][col]
specs_positions.append((row + 1, col + 1))
if specs_dict is None:
raise Exception(
"Your figures should not be drawn in an empty subplot. Please make sure to indicate the correct number of blank rows/cols"
"or colspan/rowspan going after a figure if necessary"
)
# add col_blank + row_blank positional changes and remove attributes from specs dictionary
if "col_blank" in specs_dict:
col += (specs_dict["col_blank"] + 1)
del specs_dict["col_blank"]
if "row_blank" in specs_dict:
row += (specs_dict["row_blank"] + 1)
del specs_dict["row_blank"]
specs[this_figure_row][this_figure_column] = specs_dict
# Adjust implicitly and explicity to colspan and rowspan
if "colspan" in specs_dict:
col += specs_dict["colspan"]
else:
col += 1
if "rowspan" in specs_dict:
# rowspan moves independently of col operations
row += specs_dict["rowspan"]
else:
if col >= DESIRED_COLUMNS:
row += 1
if col >= DESIRED_COLUMNS:
col = 0
else:
specs_positions = custom_positions
all_titles = []
if titles is None:
all_titles = [fig.layout.title.text if "title" in fig.layout else '' for fig in figures]
fig = make_subplots(DESIRED_ROWS, DESIRED_COLUMNS,
specs=specs,
subplot_titles=all_titles
)
assert len(specs_positions) == len(figures), "Length of specs positions not the same as number of figures"
for i, figure in enumerate(figures):
specs_row, specs_col = specs_positions[i]
for trace in figure["data"]:
fig.append_trace(
trace, row=specs_row, col=specs_col
)
col += 1
fig.update_layout(template="bnw")
return fig
# fig1 = go.Figure(go.Scatter(x=[1,2,3], y=[4,5,6], mode="lines+markers"))
# fig2 = px.line(x=[1,2,3], y=[7,8,9])
# fig2.update_traces(line=dict(color="firebrick"), mode="lines")
# remove_px_attributes(fig2, remove_hovertemplate=True, remove_mode=False)
# fig3 = px.bar(pd.DataFrame({"a" : [1,1,3], "b": [7,8,9], "c" : ["blue", "brown", "brown"]}),
# x="a", y="b", color="c")
# remove_px_attributes(fig3)
# specs=[
# [{}, {}],
# [{"colspan": 2}, None]
# ]
# figs = [fig1, fig2, fig3]
# generate_subplots(figs, custom_specs=specs, desired_rows=2)
# help(make_subplots)
figures = [fig1, fig4, fig5, fig2, fig3, fig6, fig7]
specs = [
[{"type" : "pie", "colspan" : 2}, None, {"type" : "pie", "colspan" : 2}, None, {"type" : "pie", "colspan" : 2}, None],
[{"type" : "scatter", "colspan" : 6}, None, None, None, None, None],
[{"type" : "histogram", "colspan" : 3}, None, None, {"type" : "histogram", "colspan" : 3}, None, None],
[{"type" : "box", "colspan" : 6, "rowspan" : 2}, None, None, None, None, None],
[None, None, None, None,None, None]
]
fig = generate_subplots(figures, desired_rows = 5, desired_columns = 6, custom_specs=specs)
fig.update_layout(height=1000, title=dict(
text="Linkedin Job Dataset Data Report",
xanchor="left",
x=0.05,
font_size=50
),
margin=dict(t=200),
)
go.FigureWidget(data=fig7["data"], layout=fig7["layout"])
FigureWidget({
'data': [{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Entry level',
'marker': {'color': '#FF7F0E'},
'name': 'Entry level',
'notched': False,
'offsetgroup': 'Entry level',
'orientation': 'h',
'showlegend': True,
'type': 'box',
'uid': 'c76cfe63-000c-46db-8ef5-8d65805a782a',
'x': array([ nan, nan, nan, ..., nan, 42240., 57600.]),
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Associate',
'marker': {'color': '#2CA02C'},
'name': 'Associate',
'notched': False,
'offsetgroup': 'Associate',
'orientation': 'h',
'showlegend': True,
'type': 'box',
'uid': '0b024056-e262-41ca-acc0-73fccc60a03b',
'x': array([100000., nan, 100000., ..., nan, 120000., nan]),
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Mid-Senior level',
'marker': {'color': '#D62728'},
'name': 'Mid-Senior level',
'notched': False,
'offsetgroup': 'Mid-Senior level',
'orientation': 'h',
'showlegend': True,
'type': 'box',
'uid': '24f47108-9ca3-43b6-a5b9-bc9766a04582',
'x': array([ nan, nan, nan, ..., nan, 75000., 130000.]),
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Executive',
'marker': {'color': '#9467BD'},
'name': 'Executive',
'notched': False,
'offsetgroup': 'Executive',
'orientation': 'h',
'showlegend': True,
'type': 'box',
'uid': '5ede6670-f04e-4f8b-80f9-cb5786c15f88',
'x': array([nan, nan]),
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y'}],
'layout': {'annotations': [{'ax': 0,
'ay': 0,
'font': {'size': 16},
'text': 'Sample Size: 965',
'x': 1.1,
'xanchor': 'center',
'xref': 'paper',
'y': -0.1,
'yanchor': 'top',
'yref': 'paper'}],
'boxmode': 'group',
'legend': {'title': {'text': 'Seniority level'}, 'tracegroupgap': 0},
'margin': {'t': 60},
'showlegend': True,
'template': '...',
'title': {'text': 'Salary (Lower Bound) Based on Seniority Level'},
'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'showgrid': True, 'title': {'text': 'salary_lb'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]}}
})
from ipywidgets import Button, Layout, jslink, IntText, IntSlider, GridspecLayout, HBox, VBox
grid = GridspecLayout(4, 1)
def create_expanded_button(description, button_style):
return Button(description=description, button_style=button_style, layout=Layout(height='auto', width='auto'))
for i in range(4):
for j in range(1):
grid[i, j] = create_expanded_button('Button {} - {}'.format(i, j), 'warning')
grid
GridspecLayout(children=(Button(button_style='warning', description='Button 0 - 0', layout=Layout(grid_area='w…
def convert_figures_to_figurewidgets(figures : list[go.Figure]):
return [go.FigureWidget(data=[trace.to_plotly_json() for trace in figure["data"]],
layout=figure["layout"]) for figure in figures]
def generate_gridspec(figures, custom_specs=None, desired_rows=None, desired_columns=None, titles=None,
auto_position=True, custom_positions=None):
'''
1. Convert figures into FigureWidgets
2. Prepare GridSpec
3. Traverse all figures through the specs object
a. For each figure traversed, add it to GridSpec
'''
NUM_FIGURES = len(figures)
DESIRED_ROWS = desired_rows
DESIRED_COLUMNS = desired_columns
# num_traces = sum([len(figure.data) for figure in figures])
# if desired_rows is not None:
# desired_columns = num_figures // desired_rows + 1
# if desired_columns is not None:
# desired_rows = num_figures // desired_columns + 1
specs = None
specs_positions = None
figures = convert_figures_to_figurewidgets(figures)
grid = GridspecLayout(DESIRED_ROWS, DESIRED_COLUMNS)
# find specs positions based on col_span and row_span
if custom_specs:
try:
for i, spec_row in enumerate(specs):
for j, spec_value in enumerate(spec_row):
if spec_value is None:
continue
# find first position
specs_positions.append((i+1, j+1))
raise BreakLoop("Broke out of Pythonic Loop") # Python is very bad at breaking out of a double for loop
except BreakLoop as e:
pass
row, col = specs_positions.pop()
# Work with Pythonic grid instead of Plotly grid, which starts at (1,1) top-left instead of (0,0) top-left
row -= 1
col -= 1
for i in range(NUM_FIGURES):
this_figure_row = row
this_figure_column = col
specs_dict = specs[row][col]
if specs_dict is None:
raise Exception(
"Your figures should not be drawn in an empty subplot. Please make sure to indicate the correct number of blank rows/cols"
"or colspan/rowspan going after a figure if necessary"
)
# add col_blank + row_blank positional changes and remove attributes from specs dictionary
if "col_blank" in specs_dict:
col += (specs_dict["col_blank"] + 1)
if "row_blank" in specs_dict:
row += (specs_dict["row_blank"] + 1)
# Adjust implicitly and explicity to colspan and rowspan
if "colspan" in specs_dict:
col += specs_dict["colspan"]
else:
col += 1
if "rowspan" in specs_dict:
# rowspan moves independently of col operations
row += specs_dict["rowspan"]
else:
if col >= DESIRED_COLUMNS:
row += 1
if col >= DESIRED_COLUMNS:
col = 0
if this_figure_row == row:
grid[row, this_figure_column:column] = figures[i]
else:
grid[this_figure_row:row, this_figure_column:column] = figures[i]
else:
specs = np.full((DESIRED_ROWS, DESIRED_COLUMNS), {})
k = 0
# Populate all figures from top-left to bottom-right into specs
for i in range(DESIRED_ROWS):
for j in range(DESIRED_COLUMNS):
grid[i, j] = figures[k]
k += 1
if k >= NUM_FIGURES:
break
return grid
Subplot customization has poor design in Plotly, since we can't load layouts into each subplot figure. Let's use IPyWidgets to plot instead
jup_figures = convert_figures_to_figurewidgets(figures)
c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\jupyter_client\session.py:719: UserWarning: Message serialization failed with: Out of range float values are not JSON compliant Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant
jup_figure_1 = jup_figures[0]
jup_figure_1_data = jup_figures[0]["data"]
jup_figure_1_data_json = jup_figure_1_data[0].to_plotly_json()
jup_figure_1_data_json.keys()
dict_keys(['customdata', 'domain', 'labels', 'legendgroup', 'marker', 'name', 'showlegend', 'textposition', 'values', 'type', 'uid'])
pie_charts = VBox([jup_figures[0], jup_figures[1], jup_figures[2]])
bar_charts = VBox([jup_figures[4], jup_figures[5]])
time_chart = jup_figures[3]
salary_chart_by_seniority = jup_figures[6]
report = VBox([pie_charts, bar_charts, time_chart, salary_chart_by_seniority], layout=dict(height="3000px", margin="0px 0px 0px 0px", padding="0px 0px 0px 0px"))
from IPython.display import display
display(report)
VBox(children=(VBox(children=(FigureWidget({
'data': [{'customdata': array([['Data Analyst'],
…
from ipywidgets import IntSlider
from ipywidgets.embed import embed_minimal_html, embed_data
slider = IntSlider(value=40)
embed_minimal_html('new.html', views=[slider], title='Widgets export')
from ipywidgets import IntSlider
from ipywidgets.embed import embed_minimal_html, embed_data
from IPython.display import HTML
# container = HTML('<div id="my-widget-container"></div>')
# data = embed_data(report)
# container.value = f'<script type="application/vnd.jupyter.widget-view+json">{data}</script>'
data["manager_state"]
{'version_major': 2,
'version_minor': 0,
'state': {'1dc6cc33d528457e85a6c305ff4a43c0': {'model_name': 'FigureModel',
'model_module': 'jupyterlab-plotly',
'model_module_version': '^5.13.1',
'state': {'_config': {'plotlyServerURL': 'https://plot.ly'},
'_data': [{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Entry level',
'marker': {'color': '#FF7F0E'},
'name': 'Entry level',
'notched': False,
'offsetgroup': 'Entry level',
'orientation': 'h',
'showlegend': True,
'x': {'dtype': 'float64', 'shape': (976,)},
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y',
'type': 'box',
'uid': 'c76cfe63-000c-46db-8ef5-8d65805a782a'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Associate',
'marker': {'color': '#2CA02C'},
'name': 'Associate',
'notched': False,
'offsetgroup': 'Associate',
'orientation': 'h',
'showlegend': True,
'x': {'dtype': 'float64', 'shape': (1181,)},
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y',
'type': 'box',
'uid': '0b024056-e262-41ca-acc0-73fccc60a03b'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Mid-Senior level',
'marker': {'color': '#D62728'},
'name': 'Mid-Senior level',
'notched': False,
'offsetgroup': 'Mid-Senior level',
'orientation': 'h',
'showlegend': True,
'x': {'dtype': 'float64', 'shape': (1682,)},
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y',
'type': 'box',
'uid': '24f47108-9ca3-43b6-a5b9-bc9766a04582'},
{'alignmentgroup': 'True',
'hoverinfo': 'x',
'legendgroup': 'Executive',
'marker': {'color': '#9467BD'},
'name': 'Executive',
'notched': False,
'offsetgroup': 'Executive',
'orientation': 'h',
'showlegend': True,
'x': {'dtype': 'float64', 'shape': (2,)},
'x0': ' ',
'xaxis': 'x',
'y0': ' ',
'yaxis': 'y',
'type': 'box',
'uid': '5ede6670-f04e-4f8b-80f9-cb5786c15f88'}],
'_dom_classes': (),
'_js2py_layoutDelta': None,
'_js2py_pointsCallback': {},
'_js2py_relayout': None,
'_js2py_restyle': {},
'_js2py_traceDeltas': None,
'_js2py_update': {},
'_last_layout_edit_id': 1,
'_layout': {'annotations': [{'ax': 0,
'ay': 0,
'font': {'size': 16},
'text': 'Sample Size: 965',
'x': 1.1,
'xanchor': 'center',
'xref': 'paper',
'y': -0.1,
'yanchor': 'top',
'yref': 'paper'}],
'boxmode': 'group',
'legend': {'title': {'text': 'Seniority level'}, 'tracegroupgap': 0},
'margin': {'t': 60},
'showlegend': True,
'template': {'data': {'bar': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
'type': 'bar'}],
'histogram': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
'type': 'histogram'}],
'pie': [{'hovertemplate': '<b>%{label}</b><br><i>Count</i>: %{value}',
'type': 'pie'}],
'scatter': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
'type': 'scatter'}]},
'layout': {'colorway': ['#1F77B4',
'#FF7F0E',
'#2CA02C',
'#D62728',
'#9467BD',
'#8C564B',
'#E377C2',
'#7F7F7F',
'#BCBD22',
'#17BECF'],
'dragmode': 'pan',
'font': {'size': 16},
'legend': {'font': {'size': 17}},
'paper_bgcolor': 'rgb(171,200,227)',
'plot_bgcolor': '#BAE0F3',
'showlegend': False,
'title': {'font': {'size': 19}},
'uniformtext': {'minsize': 12, 'mode': 'hide'},
'xaxis': {'showgrid': False, 'zeroline': False},
'yaxis': {'zeroline': False}}},
'title': {'text': 'Salary (Lower Bound) Based on Seniority Level'},
'xaxis': {'anchor': 'y',
'domain': [0.0, 1.0],
'showgrid': True,
'title': {'text': 'salary_lb'}},
'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]},
'autosize': True},
'_py2js_addTraces': {},
'_py2js_animate': {},
'_py2js_deleteTraces': {},
'_py2js_moveTraces': {},
'_py2js_relayout': None,
'_py2js_removeLayoutProps': {},
'_py2js_removeTraceProps': {},
'_py2js_restyle': {},
'_py2js_update': {},
'_view_count': 1},
'buffers': [{'encoding': 'base64',
'path': ['_data', 0, 'x', 'buffer'],
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embed_minimal_html('export.html', views=[time_chart], title='Widgets export')
# a = np.array([0,1,2])
# b = np.array([0,0,0])
# a = a[:, np.newaxis]
# b = b[:, np.newaxis]
# np.concatenate((b,a), axis=1)
# stemmer = SnowballStemmer('english')
# lemmatizer = WordNetLemmatizer()
# lemmatizer.lemmatize("hellothere")
Extract both lowercase and uppercase words using regex match. However, we do not want to keep some lowercase and uppercase words since regex matches may sometimes only match a part of a word, since we simply look for words instead of delimiting by a specific character, which can lead to partial matches. We need to check for those partial matches and remove them
import nltk
CHARS_TO_KEEP = "\w$%#" # Characters that should be kept because they bring semantic value
english_vocab = set(w.lower() for w in nltk.corpus.words.words())
def is_between(a : int, value : int, b : int):
'''
a: lower bound
b: upper bound
value: value to test bound
'''
return a <= value <= b
assert is_between(1, 1.5, 2) == True
assert is_between(1, 3, 2) == False
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 81 line 1 ----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=0'>1</a> import nltk <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=1'>2</a> CHARS_TO_KEEP = "\w$%#" # Characters that should be kept because they bring semantic value <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=2'>3</a> english_vocab = set(w.lower() for w in nltk.corpus.words.words()) ModuleNotFoundError: No module named 'nltk'
def deal_with_word_match_overlap(lc_word_match : re.Match, uc_word_match: re.Match,
index_num : int, should_segment=True) -> int:
'''
lc_word_match : first_match
uc_word_match : second_match
index_num : row index number of our df DataFrame
returns:
overlap State (tells us which iterator to advance):
state of 1 means second_match precedes first match
state of 0 means second_match overlaps first match
state of -1 means second_match succeeds first match
valid string: word that should be added to our final array
'''
overlap_state = -99
valid_string = ""
## Overlap found. That means only one string is valid
if is_between(lc_word_match.start(), uc_word_match.start(), lc_word_match.end()) or \
is_between(lc_word_match.start(), uc_word_match.end(), lc_word_match.end()):
if lc_word_match.string.lower() in english_vocab:
valid_string = lc_word_match.group()
elif uc_word_match.string.lower() in english_vocab:
valid_string = uc_word_match.group()
else:
if should_segment is True:
if len(lc_word_match.group()) < len(uc_word_match.group()):
valid_string = segment(uc_word_match.group())
else:
valid_string = segment(lc_word_match.group())
# print("----------------------------- WARNING -----------------------------")
# print(f"Row {index_num} with LC_match {lc_word_match}")
# print(f"and UC_match {uc_word_match} did not produce a valid string")
# print(f"Proceeding to assign valid_string to lc_word_match string")
# print("-------------------------------------------------------------------", end="\n\n")
else:
if len(lc_word_match.group()) < len(uc_word_match.group()):
valid_string = uc_word_match.group()
else:
valid_string = lc_word_match.group()
overlap_state = 0
elif uc_word_match.start() >= lc_word_match.start():
valid_string = lc_word_match.group()
overlap_state = -1
elif uc_word_match.start() <= lc_word_match.start():
valid_string = uc_word_match.group()
overlap_state = 1
return valid_string, overlap_state
## This should also find all lowercase words
def find_all_words_based_on_delim_and_capital_letters(row, test=False):
'''
Assumption: Only segment words which include capital letters
'''
job_description = row['description'] if test is False else row
lc_word_generators = itertools.tee(re.finditer(f"[{CHARS_TO_KEEP}]+", job_description))
uc_word_generators = itertools.tee(re.finditer("[A-Z]{1}\w+", job_description))
lc_word_generator_length = sum(1 for elem in lc_word_generators[0])
uc_word_generator_length = sum(1 for elem in uc_word_generators[0])
final_words_array = []
lc_match = next(lc_word_generators[1])
uc_match = next(uc_word_generators[1])
init_lc_index = 0
init_uc_index = 0
overlap_state = -99
while init_lc_index < lc_word_generator_length and \
init_uc_index < uc_word_generator_length:
# Finally, increment iterator if the above condition fits
if overlap_state == 1:
uc_match = next(uc_word_generators[1])
elif overlap_state == 0:
lc_match = next(lc_word_generators[1])
uc_match = next(uc_word_generators[1])
elif overlap_state == -1:
lc_match = next(lc_word_generators[1])
# First, check overlap status
valid_string, overlap_state = deal_with_word_match_overlap(
lc_match,
uc_match,
0) # Use 0 instead of row.name for now
if type(valid_string) == list:
final_words_array.extend(valid_string)
else:
final_words_array.append(valid_string)
# Based on overlap status, plan iterator increment
if overlap_state == 1:
init_uc_index += 1
elif overlap_state == 0:
## Only keep one of the generator words if they overlap each other
init_lc_index += 1
init_uc_index += 1
elif overlap_state == -1:
init_lc_index += 1
try:
if init_lc_index < lc_word_generator_length:
while True:
final_words_array.append(next(lc_word_generators[1]).group())
elif init_uc_index < uc_word_generator_length:
while True:
final_words_array.append(next(uc_word_generators[1]).group())
except StopIteration:
pass
np_final_word_array = np.expand_dims(np.array(final_words_array), axis=1)
np_job_description_index = np.expand_dims([0] * len(final_words_array), axis=1)
return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()
# text = "On average, I would hire a lot of coolcatseatingspaghetti for this"
# print(find_all_words_based_on_delim_and_capital_letters(text, test=True))
# text = "The candidate is expected to show up to all meetingsKey responsibilities"
# print(find_all_words_based_on_delim_and_capital_letters(text, test=True))
# words_based_on_delim = df.head(5).apply(find_all_words_based_on_delim_and_capital_letters, axis=1).explode()
def find_all_words_based_on_normal_delim(row, test=False):
'''
Assumption: Segment every word
'''
job_description = row["description"] if test is False else row
# Segment every word. Can be inefficient! How do we avoid this
all_words = []
for word in re.findall(f"[{CHARS_TO_KEEP}]+", job_description):
try:
words = segment(word) ## ValueError can occur due to unavailable segmentation
all_words.extend(words)
except ValueError:
all_words.append(word)
final_words_array = [stemmer.stem(word) for word in all_words if word.lower() not in STOPWORDS]
np_final_word_array = np.expand_dims(np.array(final_words_array), axis=1)
np_job_description_index = np.expand_dims([row.name if test is False else 0]
* len(final_words_array), axis=1)
return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()
all_words = df.swifter.apply(find_all_words_based_on_normal_delim, axis=1).explode()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 85 line 2 <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=15'>16</a> np_job_description_index = np.expand_dims([row.name if test is False else 0] <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=16'>17</a> * len(final_words_array), axis=1) <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=17'>18</a> return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist() ---> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=20'>21</a> all_words = df.swifter.apply(find_all_words_based_on_normal_delim, axis=1).explode() File c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\swifter\swifter.py:419, in DataFrameAccessor.apply(self, func, axis, raw, result_type, args, **kwds) 417 try: # try to vectorize 418 with suppress_stdout_stderr_logging(): --> 419 tmp_df = func(sample, *args, **kwds) 420 sample_df = sample.apply(func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds) 421 self._validate_apply( 422 np.array_equal(sample_df, tmp_df) & (hasattr(tmp_df, "shape")) & (sample_df.shape == tmp_df.shape), 423 error_message=("Vectorized function sample does not match pandas apply sample."), 424 ) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 85 line 8 <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=5'>6</a> # Segment every word. Can be inefficient! How do we avoid this <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=6'>7</a> all_words = [] ----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=7'>8</a> for word in re.findall(f"[{CHARS_TO_KEEP}]+", job_description): <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=8'>9</a> try: <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=9'>10</a> words = segment(word) ## ValueError can occur due to unavailable segmentation NameError: name 'CHARS_TO_KEEP' is not defined
# !pip freeze | grep spacy
# !python -m spacy download en_core_web_sm
import spacy
nlp = spacy.load("en_core_web_sm")
# [token.lemma_ for token in doc]
def find_all_words_based_on_spacy_delim(row, test=False):
'''
Assumption: Segment every word
'''
job_description = row["description"] if test is False else row
nlp_tokens = nlp(job_description)
all_words = [token.lemma_ for token in nlp_tokens if token.lemma_.lower() not in STOPWORDS]
# final_words_array = []
# ## Ignore segmentation for now because we may not need it
# for word in all_words:
# if word not in nlp_tokens.ents:
# try:
# words = segment(word) ## ValueError can occur due to unavailable segmentation
# final_words_array.extend(words)
# except ValueError:
# final_words_array.append(word)
# else:
# final_words_array.append(word)
np_final_word_array = np.expand_dims(np.array(all_words), axis=1)
np_job_description_index = np.expand_dims([row.name if test is False else 0]
* len(all_words), axis=1)
return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()
all_words = df.swifter.apply(find_all_words_based_on_spacy_delim, axis=1).explode()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 91 line 2 <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=20'>21</a> np_job_description_index = np.expand_dims([row.name if test is False else 0] <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=21'>22</a> * len(all_words), axis=1) <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=22'>23</a> return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist() ---> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=25'>26</a> all_words = df.swifter.apply(find_all_words_based_on_spacy_delim, axis=1).explode() File c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\swifter\swifter.py:419, in DataFrameAccessor.apply(self, func, axis, raw, result_type, args, **kwds) 417 try: # try to vectorize 418 with suppress_stdout_stderr_logging(): --> 419 tmp_df = func(sample, *args, **kwds) 420 sample_df = sample.apply(func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds) 421 self._validate_apply( 422 np.array_equal(sample_df, tmp_df) & (hasattr(tmp_df, "shape")) & (sample_df.shape == tmp_df.shape), 423 error_message=("Vectorized function sample does not match pandas apply sample."), 424 ) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 91 line 6 <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=1'>2</a> ''' <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=2'>3</a> Assumption: Segment every word <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=3'>4</a> ''' <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=4'>5</a> job_description = row["description"] if test is False else row ----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=5'>6</a> nlp_tokens = nlp(job_description) <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=6'>7</a> all_words = [token.lemma_ for token in nlp_tokens if token.lemma_.lower() not in STOPWORDS] <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=7'>8</a> # final_words_array = [] <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=8'>9</a> # ## Ignore segmentation for now because we may not need it <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=9'>10</a> # for word in all_words: (...) <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=16'>17</a> # else: <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=17'>18</a> # final_words_array.append(word) NameError: name 'nlp' is not defined
all_words = all_words.str[1]
words_based_on_delim = pd.DataFrame({"Label_ID": all_words.str[0],
"Word" : all_words.str[1]})
--------------------------------------------------------------------------- NameError Traceback (most recent call last) c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 94 line 1 ----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y161sZmlsZQ%3D%3D?line=0'>1</a> words_based_on_delim = pd.DataFrame({"Label_ID": all_words.str[0], <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y161sZmlsZQ%3D%3D?line=1'>2</a> "Word" : all_words.str[1]}) NameError: name 'all_words' is not defined
words_based_on_delim.head(10)
%%timeit
def get_keywords(row):
job_desc = row["description"].lower()
has_qualification = job_desc.find("qualification")
has_requirement = job_desc.find("requirement")
has_basic_requirement = job_desc.find("basic requirement")
has_responsibility = job_desc.find("responsibility")
return [has_qualification, has_requirement, has_basic_requirement, has_responsibility]
new_data = df.apply(get_keywords, result_type="expand", axis=1)
new_data = df.apply(get_keywords, result_type="expand", axis=1)
new_data.head(5)
def get_keywords_series(job_desc):
job_desc = job_desc.lower()
has_qualification = job_desc.find("qualification")
has_requirement = job_desc.find("requirement")
has_basic_requirement = job_desc.find("basic requirement")
has_responsibility = job_desc.find("responsibilit")
return [has_qualification, has_requirement, has_basic_requirement, has_responsibility]
%%timeit
series = df["description"].apply(get_keywords_series)
new_data_series = pd.DataFrame(series.tolist())
series = df["description"].apply(get_keywords_series)
new_data_series = pd.DataFrame(series.tolist(), columns=["has_qualification", "has_requirement",
"has_basic_requirement", "has_responsibility"])
new_data_series.head(5)
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
word_tokens = word_tokenize(example_sent)
# converts the words in word_tokens to lower case and then checks whether
# they are present in stop_words or not
filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]
#with no lower case conversion
filtered_sentence = []
for w in word_tokens:
if w not in stop_words:
filtered_sentence.append(w)
print(word_tokens)
print(filtered_sentence)